Build Demand Forecasts at Scale

According to McKinsey, a 10% to 20% improvement in supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues. In an environment where margins are increasingly narrow, this kind of percentage improvement can be the difference between success and failure. Traditional supply chain forecasting tools have struggled to deliver these results, limiting outcomes for retailers and manufacturers.

Traditional supply chain forecasting tools have failed to deliver the desired results, which has limited the success of retailers and manufacturers. This accelerator is designed to help retailers overcome the technical limitations of legacy data analytics solutions that undermine forecasting accuracy. It does so by leveraging distributed computational power to perform full forecasts on atomic-level data within tight service windows.

This accelerator enables fine-grained forecasting at the store-item level, constructing a forecast for each individual store-item combination. It also lets you project demand for each product across stores, giving a detailed view of expected demand rather than relying on broader, less precise aggregates. As new sales data arrives, it can efficiently generate new forecasts and append them to existing ones.

The accelerator provides pre-built code, sample data and step-by-step instructions that are ready to run in a Databricks notebook. It supports work in either Python or R, so teams can use the language that best fits their existing workflows. This setup is intended to help you move directly into fine-grained, store-item level forecasting without building the underlying infrastructure from scratch.